Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2501.02208	Robust Multi-Dimensional Scaling via Accelerated Alternating Projections	We consider the robust multi-dimensional scaling (RMDS) problem in this paper. The goal is to localize point locations from pairwise distances that may be corrupted by outliers. Inspired by classic MDS theories, and nonconvex works for the robust principal component analysis (RPCA) problem, we propose an alternating pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,398
2408.02904	Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition	This paper introduces a novel two-stage framework for accurate Egyptian Vehicle License Plate Recognition (EVLPR). The first stage employs image processing techniques to reliably localize license plates, while the second stage utilizes a custom-designed deep learning model for robust Arabic character recognition. The p...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	478,810
2406.15540	Specify What? Enhancing Neural Specification Synthesis by Symbolic Methods	We investigate how combinations of Large Language Models (LLMs) and symbolic analyses can be used to synthesise specifications of C programs. The LLM prompts are augmented with outputs from two formal methods tools in the Frama-C ecosystem, Pathcrawler and EVA, to produce C program annotations in the specification lang...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	466,772
2403.00520	IAI MovieBot 2.0: An Enhanced Research Platform with Trainable Neural Components and Transparent User Modeling	While interest in conversational recommender systems has been on the rise, operational systems suitable for serving as research platforms for comprehensive studies are currently lacking. This paper introduces an enhanced version of the IAI MovieBot conversational movie recommender system, aiming to evolve it into a rob...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	434,008
2111.04473	Senatus -- A Fast and Accurate Code-to-Code Recommendation Engine	Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevan...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	265,494
2006.10628	Offline detection of change-points in the mean for stationary graph signals	This paper addresses the problem of segmenting a stream of graph signals: we aim to detect changes in the mean of a multivariate signal defined over the nodes of a known graph. We propose an offline method that relies on the concept of graph signal stationarity and allows the convenient translation of the problem from ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,951
2502.12929	Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options	We present a novel reasoning approach called Flow-of-Options (FoO), designed to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs to systematically explore a diverse range of possibilities in their reasoning, as demonstrated by an FoO-based agentic system for autonomously solving Machine Learni...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	535,117
2006.11835	An Overview on the Landscape of R Packages for Credit Scoring	The credit scoring industry has a long tradition of using statistical tools for loan default probability prediction and domain specific standards have been established long before the hype of machine learning. Although several commercial software companies offer specific solutions for credit scorecard modelling in R ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,387
2010.10059	Very Fast Streaming Submodular Function Maximization	Data summarization has become a valuable tool in understanding even terabytes of data. Due to their compelling theoretical properties, submodular functions have been in the focus of summarization algorithms. These algorithms offer worst-case approximations guarantees to the expense of higher computation and memory requ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	201,774
2409.15261	Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning	In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field all-sky surveys. We will begin the chapter by discussing the challenges of identifying known and unknown active, extended Sol...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	490,826
1508.07192	Varying-coefficient models with isotropic Gaussian process priors	We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables. In varying-coefficient models with Gaussian process priors, a Gaussian process generates the functional relationship between the task variables and the parameters of this con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	46,385
2007.07595	Non-Relational Databases on FPGAs: Survey, Design Decisions, Challenges	Non-relational database systems (NRDS), such as graph, document, key-value, and wide-column, have gained much attention in various trending (business) application domains like smart logistics, social network analysis, and medical applications, due to their data model variety and scalability. The broad data variety and ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	187,382
2202.11864	Some Stylometric Remarks on Ovid's Heroides and the Epistula Sapphus	This article aims to contribute to two well-worn areas of debate in classical Latin philology, relating to Ovid's Heroides. The first is the question of the authenticity (and, to a lesser extent the correct position) of the letter placed fifteenth by almost every editor -- the so-called Epistula Sapphus (henceforth ES)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	282,026
1712.09473	Sketching for Kronecker Product Regression and P-splines	TensorSketch is an oblivious linear sketch introduced in Pagh'13 and later used in Pham, Pagh'13 in the context of SVMs for polynomial kernels. It was shown in Avron, Nguyen, Woodruff'14 that TensorSketch provides a subspace embedding, and therefore can be used for canonical correlation analysis, low rank approximation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	87,360
1509.08465	How Many Political Parties Should Brazil Have? A Data-driven Method to Assess and Reduce Fragmentation in Multi-Party Political Systems	In June 2013, Brazil faced the largest and most significant mass protests in a generation. These were exacerbated by the population's disenchantment towards its highly fragmented party system, which is composed by a very large number of political parties. Under these circumstances, presidents are constrained by informa...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	false	false	47,375
2312.04346	Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties	Power system cyber-physical uncertainties, including measurement ambiguities stemming from cyber attacks and data losses, along with system uncertainties introduced by massive renewables and complex dynamics, reduce the likelihood of enhancing the quality of measurements. Fortunately, denoising diffusion models exhibit...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	413,646
2207.13843	Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations	In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches by fusing pre-processing techniques into a deep learning model. Our solution accelerates training and inference performance by optimizing hyper-parameters through training instead of costly random searches to...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,418
2010.05698	Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates	In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher order continuity of the DAEM and integrates a deep autoencoder and the minimum total potential principle in one framework yielding an unsupervised fe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	200,230
2411.11536	Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure	We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices using a Poisson-gamma edge partition model, assigning each vertex to one or more latent communities through \emph{nonnegative} vertex-commun...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,092
1201.2483	Duality of Channel Encoding and Decoding - Part I: Rate-1 Binary Convolutional Codes	In this paper, we revisit the forward, backward and bidirectional Bahl-Cocke-Jelinek-Raviv (BCJR) soft-input soft-output (SISO) maximum a posteriori probability (MAP) decoding process of rate-1 binary convolutional codes. From this we establish some interesting explicit relationships between encoding and decoding of ra...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,781
1707.04987	Online Multi-Armed Bandit	We introduce a novel variant of the multi-armed bandit problem, in which bandits are streamed one at a time to the player, and at each point, the player can either choose to pull the current bandit or move on to the next bandit. Once a player has moved on from a bandit, they may never visit it again, which is a crucial...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	77,143
2201.11374	Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing	In this work, we focus on low-resource dependency parsing for multiple languages. Several strategies are tailored to enhance performance in low-resource scenarios. While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language tha...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	277,280
1701.02468	Unite the People: Closing the Loop Between 3D and 2D Human Representations	3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-the- wild". However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,555
2408.13966	Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring	Automated Short Answer Scoring (SAS) is the task of automatically scoring a given input to a prompt based on rubrics and reference answers. Although SAS is useful in real-world applications, both rubrics and reference answers differ between prompts, thus requiring a need to acquire new data and train a model for each n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	483,365
1107.1600	On fuzzy syndrome hashing with LDPC coding	The last decades have seen a growing interest in hash functions that allow some sort of tolerance, e.g. for the purpose of biometric authentication. Among these, the syndrome fuzzy hashing construction allows to securely store biometric data and to perform user authentication without the need of sharing any secret key....	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	11,201
2302.02592	RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads	To increase brand awareness, many advertisers conclude contracts with advertising platforms to purchase traffic and then deliver advertisements to target audiences. In a whole delivery period, advertisers usually desire a certain impression count for the ads, and they also expect that the delivery performance is as goo...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	344,055
2105.14156	SMASH: Sparse Matrix Atomic Scratchpad Hashing	Sparse matrices, more specifically SpGEMM kernels, are commonly found in a wide range of applications, spanning graph-based path-finding to machine learning algorithms (e.g., neural networks). A particular challenge in implementing SpGEMM kernels has been the pressure placed on DRAM memory. One approach to tackle this ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,551
2409.18402	Embed and Emulate: Contrastive representations for simulation-based inference	Scientific modeling and engineering applications rely heavily on parameter estimation methods to fit physical models and calibrate numerical simulations using real-world measurements. In the absence of analytic statistical models with tractable likelihoods, modern simulation-based inference (SBI) methods first use a nu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	492,238
2411.19058	Quality Time: Carbon-Aware Quality Adaptation for Energy-Intensive Services	The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. While hyperscalers collectively fail to meet their self-imposed emission reduction targets, they face increasing pressure from environmental sustainability reporting across many jurisdictions...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	512,104
2501.11264	Code Readability in the Age of Large Language Models: An Industrial Case Study from Atlassian	Programmers spend a significant amount of time reading code during the software development process. This trend is amplified by the emergence of large language models (LLMs) that automatically generate code. However, little is known about the readability of the LLM-generated code and whether it is still important from ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	525,858
2106.13507	Pilot Contamination Elimination for Channel Estimation with Complete Knowledge of Large-Scale Fading in Downlink Massive MIMO Systems	Massive multiple-input multiple-output is a very important technology for future fifth-generation systems. However, massive massive multiple input multiple output systems are still limited because of pilot contamination, impacting the data rate due to the non-orthogonality of pilot sequences transmitted by users in the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	243,097
2308.00918	A Novel Cross-Perturbation for Single Domain Generalization	Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. However, the limited diversity in the training data hampers the learning of domain-invariant features, resulting in compromised generalization performance. To address this, data...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,073
cs/0510063	Markerless Human Motion Capture for Gait Analysis	The aim of our study is to detect balance disorders and a tendency towards the falls in the elderly, knowing gait parameters. In this paper we present a new tool for gait analysis based on markerless human motion capture, from camera feeds. The system introduced here, recovers the 3D positions of several key points of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,031
1703.07807	Learning to Partition using Score Based Compatibilities	We study the problem of learning to partition users into groups, where one must learn the compatibilities between the users to achieve optimal groupings. We define four natural objectives that optimize for average and worst case compatibilities and propose new algorithms for adaptively learning optimal groupings. When ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,456
1802.05730	Pedestrian-Robot Interaction Experiments in an Exit Corridor	The study of human-robot interaction (HRI) has received increasing research attention for robot navigation in pedestrian crowds. In this paper, we present empirical study of pedestrian-robot interaction in an uni-directional exit corridor. We deploy a mobile robot moving in a direction perpendicular to that of the pede...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	90,489
1905.04280	A Capacity-achieving One-message Key Agreement With Finite Blocklength Analysis	Information-theoretic secret key agreement (SKA) protocols are a fundamental cryptographic primitive that are used to establish a shared secret key between two or more parties. In a two-party SKA in source model, Alice and Bob have samples of two correlated variables, that are partially leaked to Eve, and their goal is...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	130,420
1801.09665	Cooperative repair: Constructions of optimal MDS codes for all admissible parameters	Two widely studied models of multiple-node repair in distributed storage systems are centralized repair and cooperative repair. The centralized model assumes that all the failed nodes are recreated in one location, while the cooperative one stipulates that the failed nodes may communicate but are distinct, and the amou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,154
2007.12946	Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression	This paper describes the Duluth systems that participated in SemEval--2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval--2020). We participated in the three English language tasks. Our systems provide a simple Machine Learning baseline using logistic regression. We trained our mod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	188,976
1305.0556	A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure	We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation. In fact, this protocol was the main source of inspiration for this algorithm which has many applications in the area of Natural Language Processing.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	24,359
2110.14148	Uniform Concentration Bounds toward a Unified Framework for Robust Clustering	Recent advances in center-based clustering continue to improve upon the drawbacks of Lloyd's celebrated $k$-means algorithm over $60$ years after its introduction. Various methods seek to address poor local minima, sensitivity to outliers, and data that are not well-suited to Euclidean measures of fit, but many are sup...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,430
1211.2037	Time Complexity Analysis of Binary Space Partitioning Scheme for Image Compression	Segmentation-based image coding methods provide high compression ratios when compared with traditional image coding approaches like the transform and sub band coding for low bit-rate compression applications. In this paper, a segmentation-based image coding method, namely the Binary Space Partition scheme, that divides...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,644
1205.6544	A Brief Summary of Dictionary Learning Based Approach for Classification (revised)	This note presents some representative methods which are based on dictionary learning (DL) for classification. We do not review the sophisticated methods or frameworks that involve DL for classification, such as online DL and spatial pyramid matching (SPM), but rather, we concentrate on the direct DL-based classificati...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	16,229
cs/0603025	Open Answer Set Programming with Guarded Programs	Open answer set programming (OASP) is an extension of answer set programming where one may ground a program with an arbitrary superset of the program's constants. We define a fixed point logic (FPL) extension of Clark's completion such that open answer sets correspond to models of FPL formulas and identify a syntactic ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,314
2103.04116	A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies	Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,529
2201.04494	SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds	With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	275,121
2110.08598	A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer	We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledge transfer, to address acoustic mismatches between training and testing conditions. Instead of carrying out point estimation in conventional maximum a posteriori est...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	261,477
2502.12894	CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image	Recovering high-quality 3D scenes from a single RGB image is a challenging task in computer graphics. Current methods often struggle with domain-specific limitations or low-quality object generation. To address these, we propose CAST (Component-Aligned 3D Scene Reconstruction from a Single RGB Image), a novel method fo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	535,094
2303.00031	Tiny Classifier Circuits: Evolving Accelerators for Tabular Data	A typical machine learning (ML) development cycle for edge computing is to maximise the performance during model training and then minimise the memory/area footprint of the trained model for deployment on edge devices targeting CPUs, GPUs, microcontrollers, or custom hardware accelerators. This paper proposes a methodo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	348,462
1707.07151	Optimal Transmit Beamforming for Secure SWIPT in Heterogeneous Networks	This letter investigates the artificial noise aided beamforming design for secure simultaneous wireless information and power transfer (SWIPT) in a two-tier downlink heterogeneous network, where one femtocell is overlaid with one macrocell in co-channel deployment. Each energy receiver (ER) in femtocell can be consider...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,558
2303.06705	Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement	When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process. Besides, these methods usually require a tedious multi-stage training pipeline and rely on convolutional ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,960
2201.04733	Adversarially Robust Classification by Conditional Generative Model Inversion	Most adversarial attack defense methods rely on obfuscating gradients. These methods are successful in defending against gradient-based attacks; however, they are easily circumvented by attacks which either do not use the gradient or by attacks which approximate and use the corrected gradient. Defenses that do not obfu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	275,174
2011.03182	5G Embraces Satellites for 6G Ubiquitous IoT: Basic Models for Integrated Satellite Terrestrial Networks	Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation (5G) networks. However, satellites are restricted by their high lat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	205,166
2410.00485	A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning	Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content. Vision and Large Language Models (VLLM) bridge computer vision and natural language, offering numerous application...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,400
1702.08089	Quantum parameter estimation via dispersive measurement in circuit QED	We investigate the quantum parameter estimation in circuit quantum electrodynamics via dispersive measurement. Based on the Metropolis Hastings (MH) algorithm and the Markov chain Monte Carlo (MCMC) integration, a new algorithm is proposed to calculate the Fisher information by the stochastic master equation for unknow...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,913
2207.11652	Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis	Existing studies on multimodal sentiment analysis heavily rely on textual modality and unavoidably induce the spurious correlations between textual words and sentiment labels. This greatly hinders the model generalization ability. To address this problem, we define the task of out-of-distribution (OOD) multimodal senti...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	309,721
1809.02479	Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System	Recently machine learning is being applied to almost every data domain one of which is Question Answering Systems (QAS). A typical Question Answering System is fairly an information retrieval system, which matches documents or text and retrieve the most accurate one. The idea of open domain question answering system pu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	107,058
1607.00973	A fast marching algorithm for the factored eikonal equation	The eikonal equation is instrumental in many applications in several fields ranging from computer vision to geoscience. This equation can be efficiently solved using the iterative Fast Sweeping (FS) methods and the direct Fast Marching (FM) methods. However, when used for a point source, the original eikonal equation i...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	58,162
1809.10438	Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM	The automated wafer inspection and quality control is a complex and time-consuming task, which can speed up using neuromorphic memristive architectures, as a separate inspection device or integrating directly into sensors. This paper presents the performance analysis and comparison of different neuromorphic architectur...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	108,911
2007.06159	Implicit Distributional Reinforcement Learning	To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution. We adopt ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,920
2301.13362	Optimizing DDPM Sampling with Shortcut Fine-Tuning	In this study, we propose Shortcut Fine-Tuning (SFT), a new approach for addressing the challenge of fast sampling of pretrained Denoising Diffusion Probabilistic Models (DDPMs). SFT advocates for the fine-tuning of DDPM samplers through the direct minimization of Integral Probability Metrics (IPM), instead of learning...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,876
1301.3850	A Two-round Variant of EM for Gaussian Mixtures	Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	21,162
1807.04897	TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection	This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C). We observe that object candidates mined through current multiple instance learning methods are usually trapped to discriminative object pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,824
1904.06830	ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging	Grasping and manipulating objects is an important human skill. Since hand-object contact is fundamental to grasping, capturing it can lead to important insights. However, observing contact through external sensors is challenging because of occlusion and the complexity of the human hand. We present ContactDB, a novel da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,645
2402.16240	High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs	We investigate node representation learning on text-attributed graphs (TAGs), where nodes are associated with text information. Although recent studies on graph neural networks (GNNs) and pretrained language models (PLMs) have exhibited their power in encoding network and text signals, respectively, less attention has ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	432,478
1803.05084	Local Partition in Rich Graphs	Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g. people) and their connective edges (e.g. interactions). Because local graph partitioning is primarily focused on the network structure of the graph (vertices and edges), it often fails to con...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	92,569
1808.06818	A Usefulness-based Approach for Measuring the Local and Global Effect of IIR Services	In Interactive Information Retrieval (IIR) different services such as search term suggestion can support users in their search process. The applicability and performance of such services is either measured with different user-centered studies (like usability tests or laboratory experiments) or, in the context of IR, wi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	105,616
2403.00261	Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification	Recent unsupervised person re-identification (re-ID) methods achieve high performance by leveraging fine-grained local context. These methods are referred to as part-based methods. However, most part-based methods obtain local contexts through horizontal division, which suffer from misalignment due to various human pos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,919
1607.07423	A Non-Parametric Control Chart For High Frequency Multivariate Data	Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD based K-chart was first introduced by Sun and Tsung for monitoring multivariate processes when underlying distribution of process parameters or quality characteristics depart from Norm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	59,018
2010.10932	Deep learning-based citation recommendation system for patents	In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system. Although deep learning-based recommendation systems have exhibited outstanding performance in various domains (such as movies, products, and paper citations), their validity in patent citations ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	202,059
2210.08266	MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model	Food recommendation system has proven as an effective technology to provide guidance on dietary choices, and this is especially important for patients suffering from chronic diseases. Unlike other multimedia recommendations, such as books and movies, food recommendation task is highly relied on the context at the momen...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	324,067
2412.20977	UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI	We introduce UnrealZoo, a rich collection of photo-realistic 3D virtual worlds built on Unreal Engine, designed to reflect the complexity and variability of the open worlds. Additionally, we offer a variety of playable entities for embodied AI agents. Based on UnrealCV, we provide a suite of easy-to-use Python APIs and...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	521,418
2407.03152	Stereo Risk: A Continuous Modeling Approach to Stereo Matching	We introduce Stereo Risk, a new deep-learning approach to solve the classical stereo-matching problem in computer vision. As it is well-known that stereo matching boils down to a per-pixel disparity estimation problem, the popular state-of-the-art stereo-matching approaches widely rely on regressing the scene disparity...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	470,044
2409.02070	Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction	Mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations to facilitate the assessment of cardiac function and health. However, 3D medical images are often acquired as 2D slices that are sparsely sampled and noisy, and mesh reconstruction on su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,562
2312.15599	Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems	Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional increm...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	418,060
2010.05673	Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?	It is believed that a model-based approach for reinforcement learning (RL) is the key to reduce sample complexity. However, the understanding of the sample optimality of model-based RL is still largely missing, even for the linear case. This work considers sample complexity of finding an $\epsilon$-optimal policy in a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	200,220
2308.15932	Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases	Small liver lesions common to colorectal liver metastases (CRLMs) are challenging for convolutional neural network (CNN) segmentation models, especially when we have a wide range of slice thicknesses in the computed tomography (CT) scans. Slice thickness of CT images may vary by clinical indication. For example, thinne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,852
2006.15998	Distortion based Light-weight Security for Cyber-Physical Systems	In Cyber-Physical Systems (CPS), inference based on communicated data is of critical significance as it can be used to manipulate or damage the control operations by adversaries. This calls for efficient mechanisms for secure transmission of data since control systems are becoming increasingly distributed over larger g...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	184,685
2301.06986	Structural Analysis by Modified Signature Matrix for Integro-differential-algebraic Equations	Integro-differential-algebraic equations (IDAE)s are widely used in applications of engineering and analysis. When there are hidden constraints in an IDAE, structural analysis is necessary. But if derivatives of dependent variables appear in their integrals, the existing definition of the signature matrix for an IDAE c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	340,799
1809.04972	Simulation-based Distributed Coordination Maximization over Networks	In various online/offline multi-agent networked environments, it is very popular that the system can benefit from coordinating actions of two interacting agents at some cost of coordination. In this paper, we first formulate an optimization problem that captures the amount of coordination gain at the cost of node activ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	107,687
2108.08497	Monarch: A Durable Polymorphic Memory For Data Intensive Applications	3D die stacking has often been proposed to build large-scale DRAM-based caches. Unfortunately, the power and performance overheads of DRAM limit the efficiency of high-bandwidth memories. Also, DRAM is facing serious scalability challenges that make alternative technologies more appealing. This paper examines Monarch, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	251,274
1904.01775	Multimodal Representation Learning using Deep Multiset Canonical Correlation	We propose Deep Multiset Canonical Correlation Analysis (dMCCA) as an extension to representation learning using CCA when the underlying signal is observed across multiple (more than two) modalities. We use deep learning framework to learn non-linear transformations from different modalities to a shared subspace such t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,245
1905.09383	An Optimal Private Stochastic-MAB Algorithm Based on an Optimal Private Stopping Rule	We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of $\Omega \left(\frac{K\log(T)}{\epsilon}...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,716
2412.20960	Rise of Generative Artificial Intelligence in Science	Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, ...	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	521,412
2403.13825	Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation	Simulating ultra-high-granularity detector responses in Particle Physics represents a critical yet computationally demanding task. This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment, which features over 7.5M pixel channels-the highest spatial resolution detector s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	439,798
1806.09689	Convex LMI optimization for the uncertain power flow analysis	This paper investigates the uncertain power flow analysis in distribution networks within the context of renewable power resources integration such as wind and solar power. The analysis aims to bound the worst-case voltage magnitude in any node of the network for a given uncertain power generation scenario. The major d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	101,395
1905.10045	Curriculum Loss: Robust Learning and Generalization against Label Corruption	Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels. It is vitally important to reiterate robustness and generalization in DNNs against label corruption. To this end, this paper studies the 0-1 loss, which has a monotonic relationship with an empirical adversary (r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,933
1605.00855	Improving Image Captioning by Concept-based Sentence Reranking	This paper describes our winning entry in the ImageCLEF 2015 image sentence generation task. We improve Google's CNN-LSTM model by introducing concept-based sentence reranking, a data-driven approach which exploits the large amounts of concept-level annotations on Flickr. Different from previous usage of concept detect...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	55,397
2406.19531	Off-policy Evaluation with Deeply-abstracted States	Off-policy evaluation (OPE) is crucial for assessing a target policy's impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challenging. This paper studies state abstractions -- originally designed for policy learning -- in the context of OPE. Our contributions are three-f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,450
2011.12683	GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network	Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before prediction...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	208,237
2302.05098	Confidence-based Reliable Learning under Dual Noises	Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. Yet, the data collected from the open world are unavoidably polluted by noise, which may significantly undermine the efficacy of the learned mod...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	344,933
2007.04649	Learning to Reweight with Deep Interactions	Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,431
2403.01747	Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search	Conversational information-seeking (CIS) is an emerging paradigm for knowledge acquisition and exploratory search. Traditional web search interfaces enable easy exploration of entities, but this is limited in conversational settings due to the limited-bandwidth interface. This paper explore ways to rewrite answers in C...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	434,554
2409.09143	DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification	Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and practitioners often maintain and update blacklists of known malicious domains and URLs. However, blacklists frequently fail to identify emerg...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	488,194
1004.3071	Subspace Methods for Joint Sparse Recovery	We propose robust and efficient algorithms for the joint sparse recovery problem in compressed sensing, which simultaneously recover the supports of jointly sparse signals from their multiple measurement vectors obtained through a common sensing matrix. In a favorable situation, the unknown matrix, which consists of th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,194
2305.00567	Scaling Pareto-Efficient Decision Making Via Offline Multi-Objective RL	The goal of multi-objective reinforcement learning (MORL) is to learn policies that simultaneously optimize multiple competing objectives. In practice, an agent's preferences over the objectives may not be known apriori, and hence, we require policies that can generalize to arbitrary preferences at test time. In this w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	361,379
2306.03055	Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion	Using Japanese honorifics is challenging because it requires not only knowledge of the grammatical rules but also contextual information, such as social relationships. It remains unclear whether pre-trained large language models (LLMs) can flexibly handle Japanese honorifics like humans. To analyze this, we introduce a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,173
2011.02506	The dynamic effect of mechanical losses of actuators on the equations of motion of legged robots	Industrial manipulators do not collapse under their own weight when powered off due to the friction in their joints. Although these mechanism are effective for stiff position control of pick-and-place, they are inappropriate for legged robots which must rapidly regulate compliant interactions with the environment. Howe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	204,936
1203.3482	Formula-Based Probabilistic Inference	Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. Surprisingly, this problem has received little attention in the literature to date, particularly considering that it includes many standard i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	14,930
2111.11398	Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks	Self-supervised learning is a powerful paradigm for representation learning on unlabelled images. A wealth of effective new methods based on instance matching rely on data-augmentation to drive learning, and these have reached a rough agreement on an augmentation scheme that optimises popular recognition benchmarks. Ho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,653
2311.13356	Uncertainty Estimation in Multi-Agent Distributed Learning	Traditionally, IoT edge devices have been perceived primarily as low-power components with limited capabilities for autonomous operations. Yet, with emerging advancements in embedded AI hardware design, a foundational shift paves the way for future possibilities. Thus, the aim of the KDT NEUROKIT2E project is to establ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	409,715

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